Detecting deviations in clinical care in ICU data streams

描述(由申请人提供)：及时发现严重的患者病情或相关事件及其缓解仍然是临床实践中的一个重要问题。在危重病人中尤其如此[1]。为此目的开发的典型的基于计算机的检测方法依赖于临床知识的使用，例如纳入监测和警报系统的专家派生规则。然而，在现有的监测系统中提取和实施这种知识往往是耗时、昂贵和困难的。这项提案中的研究工作提供了计算性的、而不是基于专家的解决方案，这些解决方案利用存储在患者数据存储库中的数据(如电子病历)来构建警报系统。简而言之，我们的方法使用先进的机器学习算法来识别单个患者中不寻常的临床管理模式，相对于与可比患者相关的模式，并发出警报指示这种差异。我们的初步研究支持，这种偏差通常表明临床上重要的事件，值得发出警报。我们建议根据医生对从一组重症监护病房(ICU)患者病例中产生的警报的评估进行评估。项目调查人员组成了一个多学科团队，拥有重症监护医学、计算机科学、生物医学信息学、统计机器学习、基于知识的系统和临床数据存储库方面的专业知识。 公共卫生相关性：仍然有许多机会通过向临床医生发送基于计算机的提醒和警报来减少危重护理中的医疗差错。该项目使用以电子形式存储的过去患者数据和机器学习方法来帮助开发和改进基于计算机的警报，以提高医疗质量和降低成本。

DESCRIPTION (provided by applicant): Timely detection of severe patient conditions or concerning events and their mitigation remains an important problem in clinical practice. This is especially true in the critically ill patient [1]. Typical computer-based detection methods developed for this purpose rely on the use of clinical knowledge, such as expert-derived rules, that are incorporated into monitoring and alerting systems. However, it is often time-consuming, costly, and difficult to extract and implement such knowledge in existing monitoring systems. The research work in this proposal offers computational, rather than expert-based, solutions that build alert systems from data stored in patient data repositories, such as electronic medical records. Briefly, our approach uses advanced machine learning algorithms to identify unusual clinical management patterns in individual patients, relative to patterns associated with comparable patients, and raises an alert signaling this discrepancy. Our preliminary studies provide support that such deviations often indicate clinically important events for which it is worthwhile to raise an alert. We propose an evaluation based on physician assessment of alerts that are generated from a retrospective set of intensive-care unit (ICU) patient cases. The project investigators comprise a multidisciplinary team with expertise in critical care medicine, computer science, biomedical informatics, statistical machine learning, knowledge based systems, and clinical data repositories. PUBLIC HEALTH RELEVANCE: There remain numerous opportunities to reduce medical errors in critical care by sending computer-based reminders and alerts to clinicians. This project uses past patient data, which is stored in electronic form, and machine-learning methods to help develop and refine computer-based alerts to improve healthcare quality and reduce costs.